Pro‐inflammatory immunity supports fibrosis advancement in epidermolysis bullosa: intervention with Ang‐(1‐7)
Bibliographic record
Abstract
Recessive dystrophic epidermolysis bullosa (RDEB), a genetic skin blistering disease, is a paradigmatic condition of tissue fragility-driven multi-organ fibrosis. Here, longitudinal analyses of the tissue proteome through the course of naturally developing disease in RDEB mice revealed that increased pro-inflammatory immunity associates with fibrosis evolution. Mechanistically, this fibrosis is a consequence of altered extracellular matrix organization rather than that of increased abundance of major structural proteins. In a humanized system of disease progression, we targeted inflammatory cell fibroblast communication with Ang-(1-7)-an anti-inflammatory heptapeptide of the renin-angiotensin system, which reduced the fibrosis-evoking aptitude of RDEB cells. In vivo, systemic administration of Ang-(1-7) efficiently attenuated progression of multi-organ fibrosis and increased survival of RDEB mice. Collectively, our study shows that selective down-modulation of pro-inflammatory immunity may mitigate injury-induced fibrosis. Furthermore, together with published data, our data highlight molecular diversity among fibrotic conditions. Both findings have direct implications for the design of therapies addressing skin fragility and fibrosis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".